Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations12837
Missing cells52364
Missing cells (%)29.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory211.1 B

Variable types

DateTime1
Numeric8
Categorical2
Unsupported3

Alerts

snow has constant value "1.0"Constant
city is highly overall correlated with lat and 1 other fieldsHigh correlation
lat is highly overall correlated with city and 2 other fieldsHigh correlation
lon is highly overall correlated with city and 2 other fieldsHigh correlation
pres is highly overall correlated with tavg and 2 other fieldsHigh correlation
tavg is highly overall correlated with pres and 2 other fieldsHigh correlation
tmax is highly overall correlated with pres and 2 other fieldsHigh correlation
tmin is highly overall correlated with pres and 2 other fieldsHigh correlation
wspd is highly overall correlated with lat and 1 other fieldsHigh correlation
prcp has 872 (6.8%) missing valuesMissing
snow has 12836 (> 99.9%) missing valuesMissing
wdir has 12837 (100.0%) missing valuesMissing
wpgt has 12837 (100.0%) missing valuesMissing
tsun has 12837 (100.0%) missing valuesMissing
city is uniformly distributedUniform
wdir is an unsupported type, check if it needs cleaning or further analysisUnsupported
wpgt is an unsupported type, check if it needs cleaning or further analysisUnsupported
tsun is an unsupported type, check if it needs cleaning or further analysisUnsupported
prcp has 9635 (75.1%) zerosZeros

Reproduction

Analysis started2025-10-17 13:39:15.968058
Analysis finished2025-10-17 13:39:25.380577
Duration9.41 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

time
Date

Distinct1610
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size100.4 KiB
Minimum2021-02-02 00:00:00
Maximum2025-06-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-17T13:39:25.488994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:25.627442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

tavg
Real number (ℝ)

High correlation 

Distinct473
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.294368
Minimum-9
Maximum40.3
Zeros13
Zeros (%)0.1%
Negative314
Negative (%)2.4%
Memory size100.4 KiB
2025-10-17T13:39:25.781250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-9
5-th percentile3.4
Q116
median24.4
Q329.9
95-th percentile34.2
Maximum40.3
Range49.3
Interquartile range (IQR)13.9

Descriptive statistics

Standard deviation9.4987645
Coefficient of variation (CV)0.42606117
Kurtosis-0.124155
Mean22.294368
Median Absolute Deviation (MAD)6.4
Skewness-0.73018973
Sum286192.8
Variance90.226527
MonotonicityNot monotonic
2025-10-17T13:39:25.914876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.4106
 
0.8%
30.3104
 
0.8%
30.2102
 
0.8%
30.195
 
0.7%
30.891
 
0.7%
29.391
 
0.7%
30.688
 
0.7%
28.888
 
0.7%
29.783
 
0.6%
27.882
 
0.6%
Other values (463)11907
92.8%
ValueCountFrequency (%)
-91
< 0.1%
-8.11
< 0.1%
-82
< 0.1%
-7.71
< 0.1%
-7.61
< 0.1%
-7.51
< 0.1%
-7.31
< 0.1%
-7.12
< 0.1%
-6.91
< 0.1%
-6.82
< 0.1%
ValueCountFrequency (%)
40.31
 
< 0.1%
39.61
 
< 0.1%
39.52
< 0.1%
39.41
 
< 0.1%
39.31
 
< 0.1%
39.23
< 0.1%
39.13
< 0.1%
391
 
< 0.1%
38.91
 
< 0.1%
38.82
< 0.1%

tmin
Real number (ℝ)

High correlation 

Distinct464
Distinct (%)3.6%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean16.73264
Minimum-13.8
Maximum34
Zeros26
Zeros (%)0.2%
Negative747
Negative (%)5.8%
Memory size100.4 KiB
2025-10-17T13:39:26.059923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-13.8
5-th percentile-1
Q110
median18.1
Q325
95-th percentile29
Maximum34
Range47.8
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.558567
Coefficient of variation (CV)0.57125278
Kurtosis-0.40496736
Mean16.73264
Median Absolute Deviation (MAD)7.4
Skewness-0.58915085
Sum214696.5
Variance91.366203
MonotonicityNot monotonic
2025-10-17T13:39:26.201224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26239
 
1.9%
27217
 
1.7%
25213
 
1.7%
24191
 
1.5%
28185
 
1.4%
23171
 
1.3%
22158
 
1.2%
20147
 
1.1%
21142
 
1.1%
19132
 
1.0%
Other values (454)11036
86.0%
ValueCountFrequency (%)
-13.81
< 0.1%
-13.71
< 0.1%
-13.42
< 0.1%
-13.22
< 0.1%
-13.11
< 0.1%
-131
< 0.1%
-12.71
< 0.1%
-12.61
< 0.1%
-12.42
< 0.1%
-12.31
< 0.1%
ValueCountFrequency (%)
343
< 0.1%
33.81
 
< 0.1%
33.53
< 0.1%
33.42
 
< 0.1%
33.34
< 0.1%
33.22
 
< 0.1%
337
0.1%
32.91
 
< 0.1%
32.83
< 0.1%
32.71
 
< 0.1%

tmax
Real number (ℝ)

High correlation 

Distinct512
Distinct (%)4.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean28.171235
Minimum-7.7
Maximum48.8
Zeros4
Zeros (%)< 0.1%
Negative119
Negative (%)0.9%
Memory size100.4 KiB
2025-10-17T13:39:26.336931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-7.7
5-th percentile7.47
Q122.5
median30.3
Q335.5
95-th percentile41
Maximum48.8
Range56.5
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.8966538
Coefficient of variation (CV)0.35130351
Kurtosis0.41120531
Mean28.171235
Median Absolute Deviation (MAD)6.2
Skewness-0.8691486
Sum361577.8
Variance97.943757
MonotonicityNot monotonic
2025-10-17T13:39:26.732479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35230
 
1.8%
37205
 
1.6%
34199
 
1.6%
36184
 
1.4%
33174
 
1.4%
39170
 
1.3%
35.5169
 
1.3%
38167
 
1.3%
34.5158
 
1.2%
32155
 
1.2%
Other values (502)11024
85.9%
ValueCountFrequency (%)
-7.71
 
< 0.1%
-6.41
 
< 0.1%
-6.21
 
< 0.1%
-5.82
< 0.1%
-5.51
 
< 0.1%
-4.91
 
< 0.1%
-4.62
< 0.1%
-4.51
 
< 0.1%
-4.33
< 0.1%
-4.11
 
< 0.1%
ValueCountFrequency (%)
48.81
< 0.1%
48.41
< 0.1%
48.21
< 0.1%
47.51
< 0.1%
47.32
< 0.1%
47.11
< 0.1%
471
< 0.1%
46.71
< 0.1%
46.62
< 0.1%
46.52
< 0.1%

prcp
Real number (ℝ)

Missing  Zeros 

Distinct323
Distinct (%)2.7%
Missing872
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean1.5143502
Minimum0
Maximum144.1
Zeros9635
Zeros (%)75.1%
Negative0
Negative (%)0.0%
Memory size100.4 KiB
2025-10-17T13:39:26.888833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8.9
Maximum144.1
Range144.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.5279147
Coefficient of variation (CV)4.3107035
Kurtosis105.3463
Mean1.5143502
Median Absolute Deviation (MAD)0
Skewness8.6158078
Sum18119.2
Variance42.61367
MonotonicityNot monotonic
2025-10-17T13:39:27.028755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09635
75.1%
1127
 
1.0%
0.1108
 
0.8%
0.295
 
0.7%
0.380
 
0.6%
0.577
 
0.6%
263
 
0.5%
0.460
 
0.5%
353
 
0.4%
0.648
 
0.4%
Other values (313)1619
 
12.6%
(Missing)872
 
6.8%
ValueCountFrequency (%)
09635
75.1%
0.1108
 
0.8%
0.295
 
0.7%
0.380
 
0.6%
0.460
 
0.5%
0.577
 
0.6%
0.648
 
0.4%
0.739
 
0.3%
0.843
 
0.3%
0.930
 
0.2%
ValueCountFrequency (%)
144.11
< 0.1%
132.11
< 0.1%
1102
< 0.1%
109.21
< 0.1%
105.91
< 0.1%
102.12
< 0.1%
941
< 0.1%
85.11
< 0.1%
83.11
< 0.1%
811
< 0.1%

snow
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing12836
Missing (%)> 99.9%
Memory size702.1 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.01
 
< 0.1%
(Missing)12836
> 99.9%

Length

2025-10-17T13:39:27.153999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T13:39:27.228470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.01
100.0%

Most occurring characters

ValueCountFrequency (%)
11
33.3%
.1
33.3%
01
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11
33.3%
.1
33.3%
01
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11
33.3%
.1
33.3%
01
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11
33.3%
.1
33.3%
01
33.3%

wdir
Unsupported

Missing  Rejected  Unsupported 

Missing12837
Missing (%)100.0%
Memory size100.4 KiB

wspd
Real number (ℝ)

High correlation 

Distinct358
Distinct (%)2.8%
Missing68
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean10.345587
Minimum0
Maximum44
Zeros29
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size100.4 KiB
2025-10-17T13:39:27.322608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5
Q15.3
median9.1
Q314.1
95-th percentile23.8
Maximum44
Range44
Interquartile range (IQR)8.8

Descriptive statistics

Standard deviation6.7651239
Coefficient of variation (CV)0.65391397
Kurtosis0.7179358
Mean10.345587
Median Absolute Deviation (MAD)4.2
Skewness0.93117666
Sum132102.8
Variance45.766901
MonotonicityNot monotonic
2025-10-17T13:39:27.459842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5108
 
0.8%
6.3107
 
0.8%
5.2103
 
0.8%
5.8102
 
0.8%
6.497
 
0.8%
797
 
0.8%
7.496
 
0.7%
7.896
 
0.7%
7.296
 
0.7%
5.194
 
0.7%
Other values (348)11773
91.7%
ValueCountFrequency (%)
029
0.2%
0.115
 
0.1%
0.257
0.4%
0.325
 
0.2%
0.449
0.4%
0.545
0.4%
0.641
0.3%
0.719
 
0.1%
0.869
0.5%
0.928
0.2%
ValueCountFrequency (%)
441
< 0.1%
41.21
< 0.1%
39.61
< 0.1%
38.41
< 0.1%
38.31
< 0.1%
37.92
< 0.1%
37.61
< 0.1%
37.51
< 0.1%
37.31
< 0.1%
36.82
< 0.1%

wpgt
Unsupported

Missing  Rejected  Unsupported 

Missing12837
Missing (%)100.0%
Memory size100.4 KiB

pres
Real number (ℝ)

High correlation 

Distinct454
Distinct (%)3.6%
Missing69
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1010.3878
Minimum989.9
Maximum1045.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.4 KiB
2025-10-17T13:39:27.596078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum989.9
5-th percentile997.4
Q11003.6
median1010.8
Q31016.6
95-th percentile1023
Maximum1045.7
Range55.8
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.3340157
Coefficient of variation (CV)0.0082483333
Kurtosis-0.49260317
Mean1010.3878
Median Absolute Deviation (MAD)6.4
Skewness0.13997642
Sum12900632
Variance69.455818
MonotonicityNot monotonic
2025-10-17T13:39:27.740538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1013.275
 
0.6%
1017.275
 
0.6%
1015.574
 
0.6%
1015.273
 
0.6%
1016.673
 
0.6%
1014.871
 
0.6%
1016.171
 
0.6%
1016.969
 
0.5%
101369
 
0.5%
1015.968
 
0.5%
Other values (444)12050
93.9%
(Missing)69
 
0.5%
ValueCountFrequency (%)
989.91
 
< 0.1%
9911
 
< 0.1%
991.11
 
< 0.1%
991.21
 
< 0.1%
991.32
< 0.1%
991.41
 
< 0.1%
991.61
 
< 0.1%
991.73
< 0.1%
991.83
< 0.1%
991.91
 
< 0.1%
ValueCountFrequency (%)
1045.71
< 0.1%
1042.51
< 0.1%
1041.41
< 0.1%
1040.32
< 0.1%
10401
< 0.1%
1039.61
< 0.1%
1039.21
< 0.1%
1038.31
< 0.1%
10381
< 0.1%
1037.51
< 0.1%

tsun
Unsupported

Missing  Rejected  Unsupported 

Missing12837
Missing (%)100.0%
Memory size100.4 KiB

city
Categorical

High correlation  Uniform 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size703.7 KiB
Lahore
1610 
Islamabad
1610 
Hyderabad
1610 
Quetta
1609 
Multan
1609 
Other values (3)
4789 

Length

Max length9
Median length6
Mean length7.1268988
Min length6

Characters and Unicode

Total characters91488
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKarachi
2nd rowKarachi
3rd rowKarachi
4th rowKarachi
5th rowKarachi

Common Values

ValueCountFrequency (%)
Lahore1610
12.5%
Islamabad1610
12.5%
Hyderabad1610
12.5%
Quetta1609
12.5%
Multan1609
12.5%
Peshawar1608
12.5%
Skardu1591
12.4%
Karachi1590
12.4%

Length

2025-10-17T13:39:27.883121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-17T13:39:27.984434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
lahore1610
12.5%
islamabad1610
12.5%
hyderabad1610
12.5%
quetta1609
12.5%
multan1609
12.5%
peshawar1608
12.5%
skardu1591
12.4%
karachi1590
12.4%

Most occurring characters

ValueCountFrequency (%)
a20865
22.8%
r8009
 
8.8%
e6437
 
7.0%
d6421
 
7.0%
t4827
 
5.3%
u4809
 
5.3%
h4808
 
5.3%
b3220
 
3.5%
l3219
 
3.5%
s3218
 
3.5%
Other values (16)25655
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)91488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a20865
22.8%
r8009
 
8.8%
e6437
 
7.0%
d6421
 
7.0%
t4827
 
5.3%
u4809
 
5.3%
h4808
 
5.3%
b3220
 
3.5%
l3219
 
3.5%
s3218
 
3.5%
Other values (16)25655
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)91488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a20865
22.8%
r8009
 
8.8%
e6437
 
7.0%
d6421
 
7.0%
t4827
 
5.3%
u4809
 
5.3%
h4808
 
5.3%
b3220
 
3.5%
l3219
 
3.5%
s3218
 
3.5%
Other values (16)25655
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)91488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a20865
22.8%
r8009
 
8.8%
e6437
 
7.0%
d6421
 
7.0%
t4827
 
5.3%
u4809
 
5.3%
h4808
 
5.3%
b3220
 
3.5%
l3219
 
3.5%
s3218
 
3.5%
Other values (16)25655
28.0%

lat
Real number (ℝ)

High correlation 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.648902
Minimum24.8607
Maximum35.335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.4 KiB
2025-10-17T13:39:28.105383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum24.8607
5-th percentile24.8607
Q130.1575
median31.5497
Q333.6844
95-th percentile35.335
Maximum35.335
Range10.4743
Interquartile range (IQR)3.5269

Descriptive statistics

Standard deviation3.6149293
Coefficient of variation (CV)0.11794645
Kurtosis-1.1192342
Mean30.648902
Median Absolute Deviation (MAD)2.1347
Skewness-0.44665505
Sum393439.96
Variance13.067714
MonotonicityNot monotonic
2025-10-17T13:39:28.192020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
31.54971610
12.5%
33.68441610
12.5%
25.3961610
12.5%
30.17981609
12.5%
30.15751609
12.5%
34.01511608
12.5%
35.3351591
12.4%
24.86071590
12.4%
ValueCountFrequency (%)
24.86071590
12.4%
25.3961610
12.5%
30.15751609
12.5%
30.17981609
12.5%
31.54971610
12.5%
33.68441610
12.5%
34.01511608
12.5%
35.3351591
12.4%
ValueCountFrequency (%)
35.3351591
12.4%
34.01511608
12.5%
33.68441610
12.5%
31.54971610
12.5%
30.17981609
12.5%
30.15751609
12.5%
25.3961610
12.5%
24.86071590
12.4%

lon
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.040349
Minimum66.975
Maximum75.549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size100.4 KiB
2025-10-17T13:39:28.279506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum66.975
5-th percentile66.975
Q168.3578
median71.5249
Q373.0479
95-th percentile75.549
Maximum75.549
Range8.574
Interquartile range (IQR)4.6901

Descriptive statistics

Standard deviation3.0737362
Coefficient of variation (CV)0.04326747
Kurtosis-1.4101326
Mean71.040349
Median Absolute Deviation (MAD)2.8187
Skewness-0.071720082
Sum911944.96
Variance9.4478541
MonotonicityNot monotonic
2025-10-17T13:39:28.362396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
71.52493217
25.1%
73.04791610
12.5%
74.34361610
12.5%
68.35781610
12.5%
66.9751609
12.5%
75.5491591
12.4%
67.00111590
12.4%
ValueCountFrequency (%)
66.9751609
12.5%
67.00111590
12.4%
68.35781610
12.5%
71.52493217
25.1%
73.04791610
12.5%
74.34361610
12.5%
75.5491591
12.4%
ValueCountFrequency (%)
75.5491591
12.4%
74.34361610
12.5%
73.04791610
12.5%
71.52493217
25.1%
68.35781610
12.5%
67.00111590
12.4%
66.9751609
12.5%

Interactions

2025-10-17T13:39:24.001661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:16.715397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:17.601318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:18.431862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:19.494556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:20.427755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:21.364207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:22.852239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:24.098757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:16.848483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:17.699697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:18.710668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:19.611430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:20.538502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:21.541147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:22.996692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:24.195940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:16.949974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:17.794571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:18.820437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:19.725424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:20.644817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:21.690105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:23.145791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:24.295996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:17.052493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:17.903124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:18.932726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:19.844656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:20.749336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:21.857578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:23.300516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:24.403435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:17.165601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:18.010487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:19.052297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:19.963681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:20.864577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:22.020736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:23.465934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:24.500667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:17.271568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:18.111720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:19.154729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:20.075038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:20.965861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:22.419921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:23.611056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:24.600937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:17.395197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:18.218782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:19.266760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:20.193021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:21.075360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:22.564848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:23.774668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:24.700374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:17.498996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:18.322011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:19.380023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:20.312184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:21.210724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:22.712228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-17T13:39:23.910024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-17T13:39:28.447490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
citylatlonprcpprestavgtmaxtminwspd
city1.0001.0001.0000.0500.2210.3050.3120.2820.354
lat1.0001.0000.7170.2710.284-0.390-0.357-0.428-0.519
lon1.0000.7171.0000.2710.167-0.209-0.222-0.190-0.634
prcp0.0500.2710.2711.0000.004-0.159-0.233-0.076-0.076
pres0.2210.2840.1670.0041.000-0.903-0.852-0.901-0.419
tavg0.305-0.390-0.209-0.159-0.9031.0000.9700.9610.407
tmax0.312-0.357-0.222-0.233-0.8520.9701.0000.8900.372
tmin0.282-0.428-0.190-0.076-0.9010.9610.8901.0000.439
wspd0.354-0.519-0.634-0.076-0.4190.4070.3720.4391.000

Missing values

2025-10-17T13:39:24.868735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-17T13:39:25.034358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-17T13:39:25.285930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

timetavgtmintmaxprcpsnowwdirwspdwpgtprestsuncitylatlon
02021-02-0220.617.025.00.0NaNNaN10.8NaN1017.5NaNKarachi24.860767.0011
12021-02-0320.716.125.50.0NaNNaN10.9NaN1015.6NaNKarachi24.860767.0011
22021-02-0421.516.126.50.0NaNNaN19.1NaN1016.3NaNKarachi24.860767.0011
32021-02-0520.515.425.30.0NaNNaN14.1NaN1018.1NaNKarachi24.860767.0011
42021-02-0619.613.225.70.0NaNNaN12.5NaN1018.1NaNKarachi24.860767.0011
52021-02-0720.013.925.70.0NaNNaN12.3NaN1017.7NaNKarachi24.860767.0011
62021-02-0820.413.927.00.0NaNNaN11.6NaN1017.5NaNKarachi24.860767.0011
72021-02-0920.917.025.70.0NaNNaN12.0NaN1015.0NaNKarachi24.860767.0011
82021-02-1021.615.029.10.0NaNNaN10.1NaN1013.1NaNKarachi24.860767.0011
92021-02-1122.516.829.10.0NaNNaN10.0NaN1012.7NaNKarachi24.860767.0011
timetavgtmintmaxprcpsnowwdirwspdwpgtprestsuncitylatlon
128272025-06-2123.014.032.00.7NaNNaN4.3NaN1007.1NaNSkardu35.33575.549
128282025-06-2222.316.732.55.2NaNNaN4.2NaN1007.6NaNSkardu35.33575.549
128292025-06-2323.217.032.00.0NaNNaN4.7NaN1005.7NaNSkardu35.33575.549
128302025-06-2424.316.535.00.0NaNNaN4.3NaN1004.2NaNSkardu35.33575.549
128312025-06-2525.317.536.00.0NaNNaN3.9NaN1004.5NaNSkardu35.33575.549
128322025-06-2625.217.533.00.0NaNNaN5.0NaN1005.9NaNSkardu35.33575.549
128332025-06-2724.019.634.023.7NaNNaN6.3NaN1008.5NaNSkardu35.33575.549
128342025-06-2822.417.732.05.1NaNNaN3.4NaN1007.9NaNSkardu35.33575.549
128352025-06-2923.917.033.01.1NaNNaN5.3NaN1002.5NaNSkardu35.33575.549
128362025-06-3023.217.930.01.9NaNNaN5.0NaN1002.4NaNSkardu35.33575.549